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Related Concept Videos

Quality Control01:05

Quality Control

Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
Quality Assurance01:19

Quality Assurance

Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
The X̄ Chart00:58

The X̄ Chart

The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality characteristic in the order in which...
Interpreting X̄ Charts01:13

Interpreting X̄ Charts

Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line represents the process mean,...

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Related Experiment Video

Updated: Jun 12, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Self-organizing map quality control index.

Sila Kittiwachana1, Diana L S Ferreira, Louise A Fido

  • 1Centre for Chemometrics, School of Chemistry, University of Bristol, Cantocks Close, Bristol BS8 1TS, UK.

Analytical Chemistry
|June 19, 2010
PubMed
Summary

A novel self-organizing map quality control (SOMQC) index offers robust process monitoring. This method effectively detects deviations without assuming normal data distributions, enhancing quality control in pharmaceutical manufacturing.

Related Experiment Videos

Last Updated: Jun 12, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Area of Science:

  • Chemometrics
  • Process Analytical Technology (PAT)
  • Machine Learning for Quality Control

Background:

  • Traditional process monitoring methods often require assumptions of data normality or linearity.
  • Existing techniques like Q and D statistics, and support vector domain description, have limitations in handling complex, non-linear process data.
  • Principal Component Analysis (PCA) requires pre-selection of components, adding complexity to process monitoring.

Purpose of the Study:

  • To introduce and validate a new process monitoring index, the self-organizing map quality control (SOMQC) index.
  • To demonstrate the applicability of SOMQC for online monitoring of continuous pharmaceutical processes using high-performance liquid chromatography (HPLC) data.
  • To compare the performance and advantages of SOMQC against established statistical and machine learning-based process monitoring methods.

Main Methods:

  • Generation of self-organizing maps (SOMs) using normal operating condition (NOC) samples with a leave-one-out cross-validation approach.
  • Calculation of the nth percentile measured distance for left-out samples to establish a null distribution of NOC distances via the Hodges-Lehmann method.
  • Comparison of test sample distances to the SOM map against the null distribution at a specified confidence level to identify out-of-control samples.

Main Results:

  • The SOMQC index was successfully applied to online HPLC measurements from a continuous pharmaceutical process.
  • SOMQC demonstrated advantages over Q and D statistics and support vector domain description, notably not requiring multinormality or linear models.
  • The method avoids the need for PCA and provides variable importance through component planes, with tunable influence of extreme values.

Conclusions:

  • The SOMQC index provides a flexible and powerful tool for online process monitoring, particularly in pharmaceutical manufacturing.
  • Its ability to handle non-normal data and provide interpretability makes it a valuable alternative to traditional methods.
  • SOMQC enhances process control by reliably identifying deviations from normal operating conditions.